Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ahmedibrahim085/Claude-Multi-Agent-Research-System-Skill --skill multi-agent-researchergit clone --depth 1 https://github.com/ahmedibrahim085/Claude-Multi-Agent-Research-System-SkillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ahmedibrahim085/claude-multi-agent-research-system-skill/multi-agent-researcher)<a href="https://agentmods.dev/skills/ahmedibrahim085/claude-multi-agent-research-system-skill/multi-agent-researcher"><img src="https://agentmods.dev/badge/skills/ahmedibrahim085/claude-multi-agent-research-system-skill/multi-agent-researcher/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ahmedibrahim085/claude-multi-agent-research-system-skill/multi-agent-researcher"><img src="https://agentmods.dev/badge/skills/ahmedibrahim085/claude-multi-agent-research-system-skill/multi-agent-researcher.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.01837 |
| Opus 5 | $0.00026 | $0.00919 |
| Sonnet 5 | $0.00011 | $0.00367 |
| Haiku 4.5 | $0.00005 | $0.00184 |
Grade A, and why
multi-agent-researcher scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Research Coordinator
Purpose
Transform complex research questions into comprehensive reports by:
- Decomposing broad topics into 2-4 focused subtopics
- Spawning specialized researcher agents in parallel
- Synthesizing findings into cohesive final report
- Saving structured outputs for reference
When to Use
Auto-invoke when user asks:
- Search/Discovery: "Search what is [topic]", "Find information about [subject]", "Look up [technology]", "Discover [patterns]"
- Investigation: "Research [topic]", "Investigate [subject]", "Analyze [phenomenon]", "Study [field]", "Explore [domain]"
- Collection: "Gather information about [subject]", "Collect data on [topic]", "Compile resources for [area]"
- Learning: "Learn about [subject]", "Tell me about [topic]", "Dig into [technology]", "Delve into [concept]"
- Contextual: "What are the latest developments in [field]?", "Comprehensive analysis of [topic]", "Deep dive into [subject]", "State of the art in [domain]", "Best practices for [area]"
Do NOT invoke for:
- Simple factual questions ("What is the capital of France?")
- Decision evaluation ("Should I use X or Y?")
- Code-related tasks ("Debug this function", "Write a script")
Orchestration Workflow
Phase 1: Query Analysis & Decomposition
Step 1.1: Understand the Research Question Analyze user's query to identify core topic, scope, and intent.
Step 1.2: Decompose into Subtopics Break topic into 2-4 focused subtopics that are:
- Mutually exclusive (minimal overlap)
- Collectively exhaustive (cover whole topic)
- Independently researchable
- Together provide comprehensive coverage
Decomposition Patterns:
Temporal: Past → Current → Future Categorical: Category 1, 2, 3 Stakeholder: Technical → Business → Policy → User Problem-Solution: Problem → Solutions → Gaps → Future Geographic: Region A → Region B → Comparison
Step 1.3: Create Research Plan Use TodoWrite to track:
- [ ] Decompose query into subtopics
- [ ] Spawn researcher 1: [subtopic]
- [ ] Spawn researcher 2: [subtopic]
- [ ] Spawn researcher 3: [subtopic]
- [ ] Synthesize findings
- [ ] Save final report
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 257 lines · 53 tokens per session scan A f474fbc3e481
multi-agent-researcher is a skill published in the GitHub repository ahmedibrahim085/Claude-Multi-Agent-Research-System-Skill (11 stars, last pushed 8mo ago), licensed Apache-2.0. It adds 53 tokens to every session and 1,837 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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